Computers are useful functional devices which are fabricated in a variety of sizes ranging from computers which occupy large office space down to computers which are held in one's hand. These varying sizes of computers also perform an extremely wide variety of useful operations, depending on the software which is installed within their particular memory storage device. For instance, computers can manage numerous financial transactions of a bank, control the fabrication of items ranging from automobiles down to integrated circuit chips, store addresses and telephone numbers of acquaintances, enable someone to produce and edit documents, along with transmitting and receiving data. Furthermore, computers are also used to model varying responses, functions, processes, and the like, that produce a given output for a given input or inputs in order to more thoroughly understand them. For instance, computers can be used to model complex weather patterns, characteristics of a transistor within an integrated circuit, the tidal flows of the oceans throughout the world, the flow of electrons through varying diameters and types of metals, and the amount of turbulence created by an automobile at varying wind speeds.
There are different prior art methods implemented with software which enable computers to model different processes, responses, and functions. One of the typical prior art methods of computer modeling is to have the software select evenly spaced incremental data points and determine the actual results for the function at those selected incremental data points. But there are disadvantages associated with this prior art method of modeling. One of the disadvantages associated with this method is that it can require a great amount of computing time when the function to be modeled is very complex. For example, if a complex function to be modeled has many different independent variables, choosing a set increment for each variable of that function results in the number of data points to be determined scaling to the power of the number of variables. In other words, if there are 20 different independent variables of a given function and the set increment for each variable results in 100 data points for each variable, 100.sup.20 or 1.times.10.sup.22 input data points would result. As such, a significant amount of computing time can be required to calculate the respective outputs for such a large amount of input data points.
Another typical prior art method of computer modeling is referred to as design of experiment, or commonly referred to as DOE. Design of experiment is a methodology of choosing specific data points of the response to be modeled, based on the mathematical equations being used to build the model. For example, if the function to be modeled is a linear function, a design of experiment approach suggests determining a high data point and a low data point in order to model the desired response. Even though design of experiment approach is able to reduce the number of data points used to model a particular response, there are still disadvantages associated with this prior art method of modeling.
Thus, what is desired is a method for accurately building computer models of responses, functions, processes, and the like, that produce a given output for a given input or inputs. What is further desired is a method for efficiently building accurate computer models of these types of functions, responses, and processes.